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dc.contributor.author
Seeböck, Philipp
dc.contributor.author
Orlando, José Ignacio
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dc.contributor.author
Michl, Martin
dc.contributor.author
Mai, Julia
dc.contributor.author
Schmidt Erfurth, Ursula
dc.contributor.author
Bogunovic, Hrvoje
dc.date.available
2024-03-22T11:30:36Z
dc.date.issued
2024-04
dc.identifier.citation
Seeböck, Philipp; Orlando, José Ignacio; Michl, Martin; Mai, Julia; Schmidt Erfurth, Ursula; et al.; Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection; Elsevier Science; Medical Image Analysis; 93; 4-2024; 1-15
dc.identifier.issn
1361-8415
dc.identifier.uri
http://hdl.handle.net/11336/231279
dc.description.abstract
Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality or disease appearance. While several techniques have been proposed to improve them, one line of research that has not yet been investigated is the incorporation of additional semantic context through the application of anomaly detection models. In this study we experimentally show that incorporating weak anomaly labels to standard segmentation models consistently improves lesion segmentation results. This can be done relatively easy by detecting anomalies with a separate model and then adding these output masks as an extra class for training the segmentation model. This provides additional semantic context without requiring extra manual labels. We empirically validated this strategy using two in-house and two publicly available retinal OCT datasets for multiple lesion targets, demonstrating the potential of this generic anomaly guided segmentation approach to be used as an extra tool for improving lesion detection models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
deep learning
dc.subject
segmetnation
dc.subject
anomaly detection
dc.subject
semantic context
dc.subject.classification
Ingeniería de Sistemas y Comunicaciones
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dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-03-19T14:25:08Z
dc.identifier.eissn
1361-8423
dc.journal.volume
93
dc.journal.pagination
1-15
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Seeböck, Philipp. Vienna University of Technology; Austria
dc.description.fil
Fil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
dc.description.fil
Fil: Michl, Martin. Vienna University of Technology; Austria
dc.description.fil
Fil: Mai, Julia. Vienna University of Technology; Austria
dc.description.fil
Fil: Schmidt Erfurth, Ursula. Vienna University of Technology; Austria
dc.description.fil
Fil: Bogunovic, Hrvoje. Vienna University of Technology; Austria
dc.journal.title
Medical Image Analysis
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S136184152400029X
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.media.2024.103104
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